Oracle International Corporation

Kartik Kulkarni

Tirthankar Lahiri

Juan R Loaiza

Vasudha Krishnaswamy

Niloy Mukherjee

Vineet Marwah

Vivekanandhan Raja

Sukhada Pendse

Yunrui Li

Jesse Kamp

Meichun Hsu

Shasank Kisan Chavan

Garret Frederick Swart

Boris Klots

Kothanda Umamageswaran

Allison Holloway

Ajit Mylavarapu

Teck Hua Lee

Hideaki Kimura

Sanket Hase

Aurosish Mishra

Jiaqi Yan

Mohamed Zait

Mahesh Baburao Girkar

Jing Zheng

Maria Colgan

Akshay Kulkarni

Aditi Gupta

Hui Jin

Shruti Gautam

Henry Chau

Allison L Holloway

A shared-nothing database system is provided in which parallelism and workload balancing are increased by assigning the rows of each table to 'slices', and storing multiple copies (“duplicas”) of each slice across the persistent storage of multiple nodes of the shared-nothing database system. When the data for a table is distributed among the nodes of a shared-nothing system in this manner, requests to read data from a particular row of the table may be handled by any node that stores a duplica of the slice to which the row is assigned. For each slice, a single duplica of the slice is designated as the “primary duplica”. All DML operations (e.g. inserts, deletes, updates, etc.) that target a particular row of the table are performed by the node that has the primary duplica of the slice to which the particular row is assigned. The changes made by the DML operations are then propagated from the primary duplica to the other duplicas (“secondary duplicas”) of the same slice.

System and method for an ultra highly available, high performance, persistent memory optimized, scale-out database

Techniques related to query execution against an in-memory standby database are disclosed. A first database includes PF data stored on persistent storage in a persistent format. The first database is accessible to a first database server that converts the PF data to a mirror format to produce MF data that is stored within volatile memory. The first database server receives, from a second database server, one or more change records indicating one or more transactions performed against a second database. The one or more change records are applied to the PF data, and a reference timestamp is advanced from a first to a second timestamp. The first database server invalidates any MF data that is changed by a subset of the one or more transactions that committed between the first and second timestamps.

Query execution against an in-memory standby database

Techniques are described herein for executing queries on distinct portions of a database object that has been separate into chunks and distributed across the volatile memories of a plurality of nodes in a clustered database system. The techniques involve redistributing the in-memory database object portions on changes to the clustered database system. Each node may maintain a mapping indicating which nodes in the clustered database system store which chunks, and timestamps indicating when each mapping entry was created or updated. A query coordinator may use the timestamps to select a database server instance with local in memory access to data required by a portion of a query to process that portion of the query.

Method and mechanism for efficient re-distribution of in-memory columnar units in a clustered RDBMs on topology change

A shared-nothing database system is provided in which parallelism and workload balancing are increased by assigning the rows of each table to 'slices', and storing multiple copies ('duplicas') of each slice across the persistent storage of multiple nodes of the shared-nothing database system. When the data for a table is distributed among the nodes of a shared-nothing system in this manner, requests to read data from a particular row of the table may be handled by any node that stores a duplica of the slice to which the row is assigned. For each slice, a single duplica of the slice is designated as the &#x201c;primary duplica&#x201d;. All DML operations (e.g. inserts, deletes, updates, etc.) that target a particular row of the table are performed by the node that has the primary duplica of the slice to which the particular row is assigned. The changes made by the DML operations are then propagated from the primary duplica to the other duplicas (&#x201c;secondary duplicas&#x201d;) of the same slice.

Embodiments store transaction metadata in dedicated pools of allocated memory chunks. Portions of the pools of allocated memory chunks are dedicated to the respective apply slave processes that mine and process change records. Also, the pools of allocated memory chunks are anchored within the structure of a transaction log such that buffering and application of metadata for one transaction does not block required buffering and application of metadata for other transactions. The standby database system pre-processes transaction metadata in preparation for application of the metadata to invalidate appropriate portions of MF data. Further, embodiments divide the work of pre-processing invalidation records among the many apply slave processes that record the invalidation records. A garbage collection selects memory chunks for garbage collection in reverse order of how the chunks were allocated. Also, a deduplication algorithm ensures that typically only a single invalidation message per block is applied to invalidate MF data.

Space management for transactional consistency of in-memory objects on a standby database

Method and mechanism for efficient re-distribution of in-memory columnar units in a clustered RDBMS on topology change

Techniques are described herein for distributing distinct portions of a database object across volatile memories of selected nodes of a plurality of nodes in a clustered database system. The techniques involve storing a unit-to-service mapping that associates a unit (a database object or portion thereof) to one or more database services. The one or more database services are mapped to one or more nodes. The nodes to which a service is mapped may include nodes in disjoint database systems, so long as those database systems have access to a replica of the unit. The database object is treated as in-memory enabled by nodes that are associated with the service, and are treated as not in-memory enabled by nodes that are not associated with the service.

Flexible in-memory column store placement

A minimum value (MV) is computed for start timestamps that each correspond to an uncommitted transaction. In an embodiment, the MV is computed for a pluggable database that is open on at least first and second instances of a database. The MV is computed for the first instance as of a first current timestamp (CT). The MV and the first CT are communicated to a second instance that has a second CT. If the first and second CTs are equal, the second instance store the MV. If the first CT is bigger, the second CT also becomes equal to the first CT. If the first CT is smaller, the MV is discarded, and the first CT becomes equal to the second CT. In an embodiment, if the MV remains unchanged for a predetermined time period, a start timestamp corresponding to the MV is advanced to a current or future timestamp.

Efficient determination of committed changes

Techniques are described herein for executing queries on distinct portions of a database object that has been separate into chunks and distributed across the volatile memories of a plurality of nodes in a clustered database system. The techniques involve receiving a query that requires work to be performed on data that resides in a plurality of on disk extents. A parallel query coordinator that is aware of the in-memory distribution divides the work into granules that align with the in-memory separation. The parallel query coordinator then sends each granule to the database server instance with local in memory access to the data required by the granule and aggregates the results to respond to the query.

Framework for volatile memory query execution in a multi node cluster

Techniques are described herein for distributing data from one or more partitioned tables across the volatile memories of a cluster. In memory copies of data from partitioned tables are grouped based on the data falling within the same partition criteria. These groups are used for assigning data from corresponding partitions to the same node when distributing data from partitioned tables across the volatile memories of a multi-node cluster. When a query requires a join between rows of partitioned tables, the work for the join query is divided into work granules that correspond to partition-wise join operations. Those partition-wise join operations are assigned to nodes by a query coordinator based on the partition-to-node mapping located in the node of the query coordinator.

Memory-aware joins based in a database cluster

Techniques are described herein for distributing distinct portions of a database object across the volatile memories of a plurality of nodes in a clustered database system. The techniques involve establishing a single database server instance located on a node in a multi-node cluster as a load-operation master for a particular data set. The load-operation master determines how the data set may be separated into chunks using a hash function. The load-operation master then broadcasts a small payload of consistency information to other database servers, so each database server may independently execute the hash function and independently load their respectively assigned chunks of data.

Distribution of an object in volatile memory across a multi-node cluster

Growing community of inventors

Foster City, CA, United States of America

Kartik Kulkarni